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skLEP: A Slovak General Language Understanding Benchmark
Marek \v{S}uppa, Andrej Ridzik, Daniel Hl\'adek, Tom\'a\v{s} Jav\r{u}rek, Vikt\'oria Ondrejov\'a, Krist\'ina S\'asikov\'a, Martin Tamajka, Mari\'an \v{S}imko
https://arxiv.org/abs/2506.21508

skLEP: A Slovak General Language Understanding Benchmark
In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this p…

StreamLink: Large-Language-Model Driven Distributed Data Engineering System
Dawei Feng, Di Mei, Huiri Tan, Lei Ren, Xianying Lou, Zhangxi Tan
https://arxiv.org/abs/2505.21575

StreamLink: Large-Language-Model Driven Distributed Data Engineering System
Large Language Models (LLMs) have shown remarkable proficiency in natural language understanding (NLU), opening doors for innovative applications. We introduce StreamLink - an LLM-driven distributed data system designed to improve the efficiency and accessibility of data engineering tasks. We build StreamLink on top of distributed frameworks such as Apache Spark and Hadoop to handle large data at scale. One of the important design philosophies of StreamLink is to respect user data privacy by ut…

Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning
Subhadip Nandi, Neeraj Agrawal, Anshika Singh, Priyanka Bhatt
https://arxiv.org/abs/2506.01781

Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning
Customer service chatbots are conversational systems aimed at addressing customer queries, often by directing them to automated workflows. A crucial aspect of this process is the classification of the customer's intent. Presently, most intent classification models for customer care utilise only customer query for intent prediction. This may result in low-accuracy models, which cannot handle ambiguous queries. An ambiguous query like "I didn't receive my package" could indicate a delayed order, …